Such systems are finding increasing application in the entire field of information processing. They are especially highly suitable for solving problems that conventional information processing is poorly suited to solving. Examples of fields to which such systems apply that can be named are robotics, automatic pilot control, molecular research, automatic numbering of objects, and analysis and synthesis of sound.
One particularly promising field to which this type of system applies is image processing: analyzing images to recognize forms or objects, coding and decoding of images with a view to performing image compression, for example for a television system.
In the wake of numerous mathematical models, some cabled embodiments of neural networks have appeared that are arranged to be adapted to various applications. One example that can be cited is the "perceptron", which is made up of a set of processing units, called neural operators, disposed in a plurality of interconnected layers. The data to be processed are applied to the input of the first layer, and processing is done through the successive layers; the result is displayed at the output of the operators in the last layer.
The function achieved by such a network is determined by the weight of interconnections connecting the outputs of the neural operators of one layer to the inputs of those of the next layer. These weights, called "synaptic weights" by analogy with biology, can be modified by a training mechanism defined by an algorithm. This example follows the rule called HEBB, for example, and the training can be supervised or unsupervised. Another rule of training that may be mentioned is "retrograde propagation" or "gradient retropropagation".
The neural operators may be of the analog or digital type and perform relatively simple operations. Hence the analog or digital operators are intended to perform multilinear functions, that is, sums of products and possibly thresholding operations. For binary operators, the functions return to simple logic operations. Considering all the input signals of an operator and all of its synaptic weights to be a vector of input activities and a vector of coefficients, the basic operation is reduced for example to a scalar product of these two vectors. The output signal furnished by the operator defines its activity, which may be interpreted as a measure of the agreement of the signals received with a reference state defined before training by the coefficient vector.
In the context of a physical embodiment based on electronic components, it is clear that the number of input connections of one operator is necessarily limited. The result is a limitation in the number of operators capable of influencing some other given operator. In the networks known thus far, the possible interconnections among operators are predefined as a function of the application involved. The result is a lack of flexibility in configuring the network both upon initialization and in the course of its operation.